1 citations · 3 across the 7 of their papers we have counts for
5 papers · 1 filter
Slow-Fast Inference: Training-Free Inference Acceleration via Within-Sentence Support Stability
Xingyu Xie, Zhaochen Yu, Yue Liao +3
Long-context autoregressive decoding remains expensive because each decoding step must repeatedly process a growing history. We observe a consistent pattern during decoding: within…
Memory-Efficient 4-bit Preconditioned Stochastic Optimization
Jingyang Li, Kuangyu Ding, Kim-Chuan Toh +1
Preconditioned stochastic optimization algorithms, exemplified by Shampoo, outperform first-order optimizers by offering theoretical convergence benefits and practical gains in lar…
Towards Understanding Why FixMatch Generalizes Better Than Supervised Learning
Jingyang Li, Jiachun Pan, Vincent Y. F. Tan +2
Semi-supervised learning (SSL), exemplified by FixMatch (Sohn et al., 2020), has shown significant generalization advantages over supervised learning (SL), particularly in the cont…
Optimization Hyper-parameter Laws for Large Language Models
Xingyu Xie, Kuangyu Ding, Shuicheng Yan +2
Large Language Models have driven significant AI advancements, yet their training is resource-intensive and highly sensitive to hyper-parameter selection. While scaling laws provid…
LoCo: Low-Bit Communication Adaptor for Large-scale Model Training
Xingyu Xie, Zhijie Lin, Kim-Chuan Toh +1
To efficiently train large-scale models, low-bit gradient communication compresses full-precision gradients on local GPU nodes into low-precision ones for higher gradient synchroni…